Intention modeling and low-level actions alone can work well for specific straightforward tasks. However, for a more flexible robot behavior, a higher-level planner is required. MPC is becoming increasingly popular for manipulation [1]}, [2]}, and is already a standard tool for locomotion and whole-body control, capable of planning through contact models with either linear complementarity [3]} or switching dynamic modes [4]}. Contact can be considered as fully rigid constraints, using time-stepping approaches to resolve the contact impulse over an integrator time step [5]}, avoiding the issue of unbounded contact forces in rigid contact. A dynamic model for the environment has also been proposed for manipulation [6]}, relaxing kinematic constraints into a stiffness. In most contact planning situations, signed distance functions are needed to indicate the contact mode and give the contact Jacobian–this requires a geometric model typically given by CAD, although it has been learned on simplified objects [7]}.
